Overview

Dataset statistics

Number of variables9
Number of observations117221
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 MiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

df_index is highly overall correlated with yearHigh correlation
year is highly overall correlated with df_indexHigh correlation
cr_num is highly overall correlated with districtHigh correlation
district is highly overall correlated with cr_numHigh correlation
soil_moisture_min is highly overall correlated with soil_moisture_meanHigh correlation
soil_moisture_max is highly overall correlated with soil_moisture_meanHigh correlation
soil_moisture_mean is highly overall correlated with soil_moisture_min and 1 other fieldsHigh correlation
df_index is uniformly distributedUniform
df_index has unique valuesUnique
cr_num has 3852 (3.3%) zerosZeros

Reproduction

Analysis started2023-07-04 03:00:23.991730
Analysis finished2023-07-04 03:00:33.398804
Duration9.41 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct117221
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58610
Minimum0
Maximum117220
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:33.464647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5861
Q129305
median58610
Q387915
95-th percentile111359
Maximum117220
Range117220
Interquartile range (IQR)58610

Descriptive statistics

Standard deviation33838.932
Coefficient of variation (CV)0.57735766
Kurtosis-1.2
Mean58610
Median Absolute Deviation (MAD)29305
Skewness7.9260761 × 10-17
Sum6.8703228 × 109
Variance1.1450733 × 109
MonotonicityNot monotonic
2023-07-03T22:00:33.586590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
80166 1
 
< 0.1%
80164 1
 
< 0.1%
80163 1
 
< 0.1%
80161 1
 
< 0.1%
80160 1
 
< 0.1%
80159 1
 
< 0.1%
80158 1
 
< 0.1%
80157 1
 
< 0.1%
80156 1
 
< 0.1%
Other values (117211) 117211
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
117220 1
< 0.1%
117219 1
< 0.1%
117218 1
< 0.1%
117217 1
< 0.1%
117216 1
< 0.1%
117215 1
< 0.1%
117214 1
< 0.1%
117213 1
< 0.1%
117212 1
< 0.1%
117211 1
< 0.1%

year
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.7549
Minimum1978
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:33.693039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1978
5-th percentile1983
Q11991
median1997
Q32011
95-th percentile2017
Maximum2017
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.788816
Coefficient of variation (CV)0.00589808
Kurtosis-1.2228741
Mean1998.7549
Median Absolute Deviation (MAD)11
Skewness0.31329389
Sum2.3429605 × 108
Variance138.97619
MonotonicityNot monotonic
2023-07-03T22:00:33.785422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1999 9823
 
8.4%
1998 9801
 
8.4%
1997 9510
 
8.1%
2016 9320
 
8.0%
1992 9143
 
7.8%
2015 9081
 
7.7%
2011 8845
 
7.5%
2017 8657
 
7.4%
1993 7075
 
6.0%
1991 6456
 
5.5%
Other values (7) 29510
25.2%
ValueCountFrequency (%)
1978 559
 
0.5%
1982 4874
4.2%
1983 5226
4.5%
1984 4886
4.2%
1985 4958
4.2%
1986 5195
4.4%
1991 6456
5.5%
1992 9143
7.8%
1993 7075
6.0%
1994 3812
3.3%
ValueCountFrequency (%)
2017 8657
7.4%
2016 9320
8.0%
2015 9081
7.7%
2011 8845
7.5%
1999 9823
8.4%
1998 9801
8.4%
1997 9510
8.1%
1994 3812
 
3.3%
1993 7075
6.0%
1992 9143
7.8%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2727412
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:33.883637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median7
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4542377
Coefficient of variation (CV)0.33745703
Kurtosis-0.80117132
Mean7.2727412
Median Absolute Deviation (MAD)2
Skewness-0.067768112
Sum852518
Variance6.0232826
MonotonicityNot monotonic
2023-07-03T22:00:33.970650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 15140
12.9%
5 14861
12.7%
8 14810
12.6%
9 14788
12.6%
6 14540
12.4%
10 13561
11.6%
4 11745
10.0%
11 9000
7.7%
3 3779
 
3.2%
12 3138
 
2.7%
Other values (2) 1859
 
1.6%
ValueCountFrequency (%)
1 880
 
0.8%
2 979
 
0.8%
3 3779
 
3.2%
4 11745
10.0%
5 14861
12.7%
6 14540
12.4%
7 15140
12.9%
8 14810
12.6%
9 14788
12.6%
10 13561
11.6%
ValueCountFrequency (%)
12 3138
 
2.7%
11 9000
7.7%
10 13561
11.6%
9 14788
12.6%
8 14810
12.6%
7 15140
12.9%
6 14540
12.4%
5 14861
12.7%
4 11745
10.0%
3 3779
 
3.2%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.879313
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:34.069522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.847425
Coefficient of variation (CV)0.55716672
Kurtosis-1.2057331
Mean15.879313
Median Absolute Deviation (MAD)8
Skewness-0.0094467519
Sum1861389
Variance78.276929
MonotonicityNot monotonic
2023-07-03T22:00:34.167497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25 4060
 
3.5%
26 4012
 
3.4%
29 3976
 
3.4%
27 3945
 
3.4%
23 3916
 
3.3%
16 3904
 
3.3%
13 3887
 
3.3%
10 3878
 
3.3%
11 3878
 
3.3%
12 3866
 
3.3%
Other values (21) 77899
66.5%
ValueCountFrequency (%)
1 3851
3.3%
2 3720
3.2%
3 3834
3.3%
4 3820
3.3%
5 3833
3.3%
6 3739
3.2%
7 3785
3.2%
8 3745
3.2%
9 3689
3.1%
10 3878
3.3%
ValueCountFrequency (%)
31 2241
1.9%
30 3827
3.3%
29 3976
3.4%
28 3842
3.3%
27 3945
3.4%
26 4012
3.4%
25 4060
3.5%
24 3863
3.3%
23 3916
3.3%
22 3735
3.2%

cr_num
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2548861
Minimum0
Maximum10
Zeros3852
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:34.262254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7631007
Coefficient of variation (CV)0.52581553
Kurtosis-0.93618946
Mean5.2548861
Median Absolute Deviation (MAD)2
Skewness-0.080872823
Sum615983
Variance7.6347254
MonotonicityNot monotonic
2023-07-03T22:00:34.350641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 14465
12.3%
5 14370
12.3%
6 13997
11.9%
4 13702
11.7%
8 10932
9.3%
1 10832
9.2%
3 10643
9.1%
9 9065
7.7%
10 7695
6.6%
2 7668
6.5%
ValueCountFrequency (%)
0 3852
 
3.3%
1 10832
9.2%
2 7668
6.5%
3 10643
9.1%
4 13702
11.7%
5 14370
12.3%
6 13997
11.9%
7 14465
12.3%
8 10932
9.3%
9 9065
7.7%
ValueCountFrequency (%)
10 7695
6.6%
9 9065
7.7%
8 10932
9.3%
7 14465
12.3%
6 13997
11.9%
5 14370
12.3%
4 13702
11.7%
3 10643
9.1%
2 7668
6.5%
1 10832
9.2%

district
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4730.5855
Minimum4601
Maximum4870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:34.451686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4601
5-th percentile4603
Q14612
median4741
Q34791
95-th percentile4860
Maximum4870
Range269
Interquartile range (IQR)179

Descriptive statistics

Standard deviation87.887526
Coefficient of variation (CV)0.018578572
Kurtosis-1.1830538
Mean4730.5855
Median Absolute Deviation (MAD)69
Skewness-0.25589119
Sum5.5452396 × 108
Variance7724.2172
MonotonicityNot monotonic
2023-07-03T22:00:34.566359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4840 5808
 
5.0%
4870 3875
 
3.3%
4612 3852
 
3.3%
4860 3820
 
3.3%
4830 3751
 
3.2%
4820 3637
 
3.1%
4810 3544
 
3.0%
4850 3257
 
2.8%
4790 3175
 
2.7%
4740 2997
 
2.6%
Other values (29) 79505
67.8%
ValueCountFrequency (%)
4601 2657
2.3%
4602 2686
2.3%
4603 2626
2.2%
4604 2464
2.1%
4605 2523
2.2%
4606 2681
2.3%
4607 2808
2.4%
4608 2674
2.3%
4609 2666
2.3%
4610 2852
2.4%
ValueCountFrequency (%)
4870 3875
3.3%
4860 3820
3.3%
4850 3257
2.8%
4840 5808
5.0%
4830 3751
3.2%
4820 3637
3.1%
4810 3544
3.0%
4791 2911
2.5%
4790 3175
2.7%
4781 2647
2.3%

soil_moisture_min
Real number (ℝ)

Distinct114629
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16165965
Minimum0
Maximum0.64574057
Zeros123
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:34.745436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.096687213
Q10.12938167
median0.15652949
Q30.18990871
95-th percentile0.24168602
Maximum0.64574057
Range0.64574057
Interquartile range (IQR)0.060527042

Descriptive statistics

Standard deviation0.045153557
Coefficient of variation (CV)0.27931247
Kurtosis0.98062917
Mean0.16165965
Median Absolute Deviation (MAD)0.029800057
Skewness0.59812647
Sum18949.906
Variance0.0020388437
MonotonicityNot monotonic
2023-07-03T22:00:34.866810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 123
 
0.1%
0.08241999894 19
 
< 0.1%
0.1087899953 18
 
< 0.1%
0.090640001 17
 
< 0.1%
0.07685999572 17
 
< 0.1%
0.07482999563 15
 
< 0.1%
0.1005899981 15
 
< 0.1%
0.07521999627 14
 
< 0.1%
0.08415000141 14
 
< 0.1%
0.08810000122 13
 
< 0.1%
Other values (114619) 116956
99.8%
ValueCountFrequency (%)
0 123
0.1%
0.001210424467 1
 
< 0.1%
0.002227913588 1
 
< 0.1%
0.003510870039 1
 
< 0.1%
0.005049052183 1
 
< 0.1%
0.009607681073 1
 
< 0.1%
0.01146424375 1
 
< 0.1%
0.01209984161 1
 
< 0.1%
0.01422919892 1
 
< 0.1%
0.01498914883 1
 
< 0.1%
ValueCountFrequency (%)
0.6457405686 1
< 0.1%
0.5618879199 1
< 0.1%
0.5259379745 1
< 0.1%
0.5016546845 1
< 0.1%
0.4543149173 1
< 0.1%
0.452249974 1
< 0.1%
0.4516600072 1
< 0.1%
0.4365988672 1
< 0.1%
0.4365700185 1
< 0.1%
0.4353488088 1
< 0.1%

soil_moisture_max
Real number (ℝ)

Distinct115117
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2663669
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:34.994200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1718033
Q10.22146627
median0.26009038
Q30.30534714
95-th percentile0.38160306
Maximum1
Range1
Interquartile range (IQR)0.083880872

Descriptive statistics

Standard deviation0.063959205
Coefficient of variation (CV)0.24011694
Kurtosis1.6882324
Mean0.2663669
Median Absolute Deviation (MAD)0.041546434
Skewness0.64726641
Sum31223.794
Variance0.0040907799
MonotonicityNot monotonic
2023-07-03T22:00:35.115008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4679999948 89
 
0.1%
0.1673100144 9
 
< 0.1%
0.4390000105 9
 
< 0.1%
0.2333600074 9
 
< 0.1%
0.3050799966 8
 
< 0.1%
0.3491100073 7
 
< 0.1%
0.4331700206 7
 
< 0.1%
1 6
 
< 0.1%
0.452249974 6
 
< 0.1%
0.3685699701 6
 
< 0.1%
Other values (115107) 117065
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.04361159354 1
< 0.1%
0.05770999938 1
< 0.1%
0.07018999755 1
< 0.1%
0.07067909092 1
< 0.1%
0.07183000445 1
< 0.1%
0.07389040291 1
< 0.1%
0.07664000243 1
< 0.1%
0.07676000148 1
< 0.1%
0.07722999901 1
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.8029572368 1
 
< 0.1%
0.7982676029 1
 
< 0.1%
0.7946008444 1
 
< 0.1%
0.7860453129 1
 
< 0.1%
0.7845153213 1
 
< 0.1%
0.7719236016 1
 
< 0.1%
0.7685557008 1
 
< 0.1%
0.7459300756 1
 
< 0.1%
0.7408373356 1
 
< 0.1%

soil_moisture_mean
Real number (ℝ)

Distinct117189
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20791962
Minimum0
Maximum0.64574057
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size915.9 KiB
2023-07-03T22:00:35.235770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14314213
Q10.17573811
median0.2041316
Q30.2366064
95-th percentile0.28421264
Maximum0.64574057
Range0.64574057
Interquartile range (IQR)0.060868291

Descriptive statistics

Standard deviation0.043866671
Coefficient of variation (CV)0.21097899
Kurtosis0.37925091
Mean0.20791962
Median Absolute Deviation (MAD)0.030183053
Skewness0.48451513
Sum24372.545
Variance0.0019242849
MonotonicityNot monotonic
2023-07-03T22:00:35.353340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2815699875 4
 
< 0.1%
0.2452364564 2
 
< 0.1%
0.1660926491 2
 
< 0.1%
0.2556999922 2
 
< 0.1%
0.1479144692 2
 
< 0.1%
0.1952497363 2
 
< 0.1%
0.1729080379 2
 
< 0.1%
0.2189272493 2
 
< 0.1%
0.2862062156 2
 
< 0.1%
0.1679622829 2
 
< 0.1%
Other values (117179) 117199
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.04361159354 1
< 0.1%
0.05770999938 1
< 0.1%
0.07018999755 1
< 0.1%
0.07067909092 1
< 0.1%
0.07183000445 1
< 0.1%
0.07389040291 1
< 0.1%
0.07664000243 1
< 0.1%
0.07676000148 1
< 0.1%
0.07722999901 1
< 0.1%
ValueCountFrequency (%)
0.6457405686 1
< 0.1%
0.5618879199 1
< 0.1%
0.5259379745 1
< 0.1%
0.5016546845 1
< 0.1%
0.4543149173 1
< 0.1%
0.452249974 1
< 0.1%
0.4516600072 1
< 0.1%
0.4437993914 1
< 0.1%
0.4414606132 1
< 0.1%
0.4365988672 1
< 0.1%

Interactions

2023-07-03T22:00:32.072379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:24.762235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.747176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.667991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.550922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.451678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.394270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.278876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.184459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.239942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:24.943695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.850772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.765299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.650943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.548733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.494858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.380262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.283957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.345474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.049850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.956215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.868189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.754061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.718207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.598821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.487271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.387315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.442538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.147529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.056721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.960866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.850226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.810268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.693203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.584160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.484047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.543600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.247846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.158623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.064515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.947289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.914904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.791855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.685342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.582676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.640050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.345241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.258234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.156745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.049782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.006798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.886136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.782744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.678509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.739542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.444344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.358305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.258211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.146257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.101025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.981183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.882287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.774670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.841791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.545945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.463370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.356540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.247745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.200528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.080937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.983015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.875338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:32.939721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:25.644650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:26.563576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:27.452075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:28.344050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:29.294872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:30.178258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.082756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-03T22:00:31.971045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-03T22:00:35.455060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
df_indexyearmonthdaycr_numdistrictsoil_moisture_minsoil_moisture_maxsoil_moisture_mean
df_index1.0000.9980.017-0.002-0.056-0.0600.174-0.0340.070
year0.9981.000-0.048-0.003-0.054-0.0590.190-0.0180.090
month0.017-0.0481.000-0.106-0.028-0.024-0.235-0.238-0.300
day-0.002-0.003-0.1061.0000.0000.000-0.005-0.001-0.003
cr_num-0.056-0.054-0.0280.0001.0000.951-0.210-0.037-0.169
district-0.060-0.059-0.0240.0000.9511.000-0.2080.047-0.104
soil_moisture_min0.1740.190-0.235-0.005-0.210-0.2081.0000.3950.767
soil_moisture_max-0.034-0.018-0.238-0.001-0.0370.0470.3951.0000.830
soil_moisture_mean0.0700.090-0.300-0.003-0.169-0.1040.7670.8301.000

Missing values

2023-07-03T22:00:33.081940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-03T22:00:33.259807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

df_indexyearmonthdaycr_numdistrictsoil_moisture_minsoil_moisture_maxsoil_moisture_mean
001978111046120.1659000.4187100.237053
111978111547400.1271400.2072480.163722
221978111547410.1533980.2153040.170989
331978111747700.1407720.1863900.152211
441978111747710.1168100.1168100.116810
551978111747910.2202730.2202730.220273
661978111848100.1014900.2878310.180286
771978111848200.1357910.2810930.186174
881978111848300.1440740.2880500.193872
991978111948400.1257570.2272330.165919
df_indexyearmonthdaycr_numdistrictsoil_moisture_minsoil_moisture_maxsoil_moisture_mean
11721111681620171021948400.2030000.2506360.226508
11721211684120171022948400.2218130.2259970.223948
11721311686920171023948400.1890070.2570020.215051
11721411688920171024948400.2217890.2629520.236139
11721511691120171025948400.1963220.2563640.220181
11721611693820171026948400.1913390.2710520.220148
11721711695720171027948400.2514810.2514810.251481
11721811697620171028948400.1931650.2675070.223290
11721911700320171029948400.1915070.2430240.208214
1172201170332017111948400.2030480.2141180.208583